Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI

Abstract

Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by motor and vocal tics. Individuals with TS would benefit greatly from advances in prediction of symptom timecourse and treatment effectiveness. As a first step, we applied a multivariate method – support vector machine (SVM) classification – to test whether patterns in brain network activity, measured with resting state functional connectivity (RSFC) MRI, could predict diagnostic group membership for individuals. RSFC data from 42 children with TS (8–15 yrs) and 42 unaffected controls (age, IQ, in‐scanner movement matched) were included. While univariate tests identified no significant group differences, SVM classified group membership with ~70% accuracy (p < .001). We also report a novel adaptation of SVM binary classification that, in addition to an overall accuracy rate for the SVM, provides a confidence measure for the accurate classification of each individual. Our results support the contention that multivariate methods can better capture the complexity of some brain disorders, and hold promise for predicting prognosis and treatment outcome for individuals with TS.

Disrupted topological organization of structural networks revealed by probabilistic diffusion tractography in Tourette syndrome children

“…We implemented feature selection algorithm on the original features without between-group statistical comparison to select significantly altered features first, which can ensure SVM results have good generalization ability as in our previous study [Wen et al, 2017a]. The classification performances with higher accuracy, sensitivity, and specificity in our study than the previous study [Greene et al, 2016] have shown that the integration of multiple kernels not only increases the classification accuracy but also enhances the interpretability of the results. For each feature, we also counted its selection frequency by SVM-RFE method in nested CV procedure, to reveal the discriminative features for classification.…”

“…Unlike groupbased comparison approaches, machine-learning techniques are able to detect the fine-grained spatial discriminative patterns, which are critical for individual-based disease diagnosis [Liu et al, 2014]. Especially, many studies have applied machine learning methods to investigate brain functional or structural networks for assisting clinical disease diagnosis [Dai et al, 2012[Dai et al, , 2013Jie et al, 2014;Jin et al, 2015;Sacchet et al, 2015], however, only a few of them [Greene et al, 2016] are related to TS. To date, there are no reliable neuromarkers for clinical TS diagnosis, and TS is still misdiagnosed due to its complicated clinical presentation [Cavanna and Seri, 2013].…”

Precision Functional Mapping of Individual Human Brains

“…So far, the quantity of RSFC data typically collected in patients (5–15 min) is adequate for pre-surgical planning around a few brain networks, but insufficient for precisely mapping function. To make predictions about individuals, machine learning algorithms can classify subjects as patients or controls using small quantities of data (Fair et al, 2013; Greene et al, 2016). However, these classification approaches have yet to be translated into routine clinical use.…”

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

“…These studies have examined both structural and functional network organization in a wide variety of samples, including healthy young adults (Power et al, 2013;Zanto and Gazzaley, 2013), developmental cohorts (Gu et al, 2015;Nielsen et al, 2018;Rudolph et al, 2017), older adults (Baniqued et al, 2018;Gallen et al, 2016), and a plethora of neurological and psychiatric populations (Gratton et al, 2018a;Greene et al, 2016;Sheffield et al, 2015;Siegel et al, 2018). We have gained a better understanding of typical and atypical human brain organization from these efforts.…”

“…These two ROI sets sample the cortex well, representing a diverse set of brain areas that can be organized into functional networks. Many investigators have used them to describe functional brain organization in a variety of healthy samples (Power et al, 2013;Zanto and Gazzaley, 2013), lifespan cohorts (Baniqued et al, 2018;Gallen et al, 2016;Gu et al, 2015;Nielsen et al, 2018;Rudolph et al, 2017), as well as populations with neurologic and psychiatric diseases (Gratton et al, 2018a;Greene et al, 2016;Sheffield et al, 2015;Siegel et al, 2018). However, the first set (264 volumetric ROIs) under-samples subcortical and cerebellar structures, as only 17 ROIs are non-cortical, and the second set (333 parcels) is restricted to the cortex only, similar to other popular ROI sets, e.g.…”